Performance Evaluation of Vector Evaluated Gravitational Search Algorithms Based on ZDT Test Functions - Badaruddin, Muhammad; Zuwairie, Ibrahim; Kamarul Hawari, Ghazali; Mohd Riduwan, Ghazali; Kian, Sheng Lim; Sophan Wahyudi, Nawawi; Nor Azlina, Ab. Aziz; Marizan, Mubin; Norrima, Mokhtar (United Kingdom Simulation Society, 2014)
This paper presents a performance evaluation of Vector Evaluated Gravitational Search Algorithm (VEGSA), namely VEGSA-I and VEGSA-II algorithms, for multi-objective optimization problems. The VEGSA algorithms use a number of populations of particles. In particular, a population of particles corresponds to one objective function to be minimized or maximized. Simultaneous minimization or maximization of every objective function is realized by exchanging a variable between populations. Performance evaluation is done based on ZDT test functions, which is a common benchmark problem for multiobjective optimization. The results shows that both VEGSA algorithms are outperformed by other multi-objective optimization algorithms and further enhancements are needed before it can be employed in any application.